When an account holder makes a purchase from a merchant, a payment card corresponding to the account can be used to pay for the transaction. The merchant forwards the financial transaction information to an acquiring bank (herein referred to as the “acquirer”). A payment processor (such as Visa®, MasterCard®, American Express®, etc.) receives the transaction information and then forwards it to the payment card issuing bank (the “issuer”) for approval. The issuer decides on whether or not to approve the cardholder's purchase. The existing model requires that issuers have a great deal of technical infrastructure in order to support payment cards. Additionally, maintaining the technical infrastructure is both expensive and difficult, as issuers must monitor and react to various types of payment card fraud. Issuers suffer a great deal of losses due to fraud schemes.
Various business rules are used to manage the risk of authorizing a fraudulent transaction. An example of a business rule would be to decline any attempt to withdrawal currency from an Automatic Teller Machine (ATM) if: (i) a risk score, calculated according to a predetermined algorithm using one or more predetermined parameters, exceeds a predetermined threshold, and (ii) the currency to be withdrawn from the ATM exceeds a predetermined threshold.
Financial institutions, however, often lack the expertise to effectively write fraud strategies in terms of business rules. Moreover, developing fraud reduction strategies are often manual and rely heavily on human intuition. Accordingly, it would be an advance in the relevant art to allow a user to input desired optimization parameters for business rules for a fraud reduction strategy, and to use the user's input to produce a recommended set of business rules that are based upon predetermined fraud and authorization strategies using data driven models. Thereafter, it would further be an advance in the relevant art to provide the user with tools to intelligently monitor and update fraud reduction strategies through modification to existing business rules. Moreover, it would be an advance in the art to use these authorization optimization strategy tools to implement more targeted strategies for declining requests to authorize transactions, and to identify strategies to approve more low risk transactions.